Layer 16 · Neuron 7407 · suppressed

L16N7407-1

Word quirks:

“ simpler”“ simplified”“ simple”“ similarly”“ similar”“ even”“ simplicity”“ basic”

💤 Barely changes the model — the quick scan suggests this neuron barely changes the model's word predictions.

Identity (according to the specimen itself)

I am Qwen3.5, a large language model designed to assist with complex tasks. My core purpose is to provide intelligent support across a wide range of capabilities, including reasoning, coding, and multilingual interaction. Here's how I operate:

Core Function: To act as a versatile assistant that handles tasks requiring precision, adaptability, and deep understanding.
Key Focus Areas:
Specialized Reasoning: Designed for structured problem-solving in text, code, or data analysis.
Multilingual Support: Fluent in over 100 languages, enabling seamless communication across barriers.

Nearest neighbors

Specimens with similar replies or word quirks. Similarity runs from −1 to 1; 1 is identical. These are leads, not shared mechanisms. How profiles are built.

By word quirks Profile recipe

Specimen	Similarity	Word quirks
1. L15N4861-1🕒	0.92	“ simple”“ basic”“ minimal”
2. L14N2575-1💤	0.90	“⏎”“ similar”“ simple”
3. L16N9023+1🕒	0.88	“ simpler”“ simplicity”“ simple”
4. L12N8492-1💤	0.88	“ simpler”“ simple”“ simplicity”
5. L15N7489+1🕒	0.88	“ simple”“ simply”“ simpler”
6. L15N7944+1🕒	0.88	“ simply”“ simple”“ simpler”
7. L5N8154-1🕒	0.87	“ simplicity”“ simple”“ simpler”
8. L14N6650-1💤	0.87	“ simple”“ simpler”“ imaginary”
9. L15N8848-1🕒	0.87	“ similarly”“ simpler”“ straightforward”
10. L11N2620+1🕒	0.86	“ easy”“ simplified”“ simple”

Shadow specimen, apart from the ten: L16N7407+1🕒, -0.18.

Cousins · specimens sharing a neuron

No specimen with replies shares this neuron yet.

Technical details

Quick scan: measure prediction shifts on fixed reference text. Short test: generate replies to a small set of prompts. Longer test: extend to a larger set. Full test: run the full prompt suite. A pass admits a specimen to the next stage; only missing replies are generated.

Layer 16, neuron 7407, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 0.56, top-1 agreement 0.72) is outside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), so it stays at the quick scan unless someone queues it by hand. A hand pick goes straight to the full test. See Method for the tiers.

Quick scan · prediction measurements

Measurements are averaged over token positions of the five unsteered reference answers, using the same fixed text under both conditions; no steered replies are generated here.

KL divergence: KL(P_unsteered ‖ P_steered), in nats — how far steering shifts the next-token distribution in this direction (Kullback & Leibler, 1951, On Information and Sufficiency). Unsteered ≈ 0.001.
Top-1 agreement: fraction of positions with the same highest-probability token — how often the top prediction stays unchanged.
ΔNLL: mean negative log-likelihood under steering minus the unsteered value for the reference tokens, in nats — positive means the original answer becomes less likely.
Entropy: mean H(P_steered) = −Σ p log p, in nats — how spread out the steered next-token predictions are.

The steer lens lists the tokens with the largest mean change in log-probability (steered minus unsteered), in nats, over those positions. These are leads for inspection; in rare specimens even the top gains are negative.

Setting	KL divergence	Top-1 agreement	ΔNLL	Entropy	Steer lens
L16N7407+1🕒 shadow specimen t0-v0.1	0.748	0.68	0.836	1.22	“ __”“ ___”“ ”“ ¿”“<\|im_end\|>”“ ____”“ __________________”“ ???”
L16N7407-1 t0-v0.1	0.564	0.72	0.734	1.21	Top boosted tokens ↑

Method

A specimen is a fixed steering configuration of Qwen3.5-4B: one MLP neuron or a pair, each with a signed dose. Unsteered is the control. L identifies the layer; N, the neuron.

Additive activation steering. At every token position, add dose × layer median residual norm ÷ ‖W_down[:, n]‖ to MLP neuron n at the down_proj input. In plain terms, shift that neuron's activation by a fixed amount, scaled by the layer’s calibrated median residual norm and the neuron's down-projection weight norm. Positive doses push up; negative doses push down. Dose is normalized intervention size, not a percentage or probability.